Location: Crop Production Systems ResearchTitle: Simulation of crop evapotranspiration and crop coefficient with data in weighing lysimeters Author
|Evett, Steven - Steve|
Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/6/2016
Publication Date: 8/6/2016
Citation: Anapalli, S.S., Ahuja, L.R., Gowda, P., Ma, L., Marek, G.W., Evett, S.R., Howell, T.A. 2016. Simulation of crop evapotranspiration and crop coefficient with data in weighing lysimeters. Agricultural Water Management. 177:274-283.
Interpretive Summary: In the days to come, irrigation water scarcity is expected to increase further not only from competing human enterprises (for example, drinking, sanitation, urban irrigation, industry, and ecosystem services) but also from global warming arising from increasing greenhouse gas in the atmosphere and associated altered precipitation patterns and greater frequency of severe droughts. In the emerging scenario, there is widespread human concern that crop productivity will decline in the coming years when the demand for produces is virtually escalating. To obtain an optimum return from limited water (rainfall and irrigation) available for irrigation, producers need whole-system based quantitative knowledge and information on crop water demands and supply for precision water management on site-specific or field-specific bases. Scientists at the Crop Production Systems Research Unit, Stoneville, MS in collaboration with scientists at Agricultural Systems Research Unit, Fort Collins, CO and Conservation and Production Research Laboratory, Bushland, TX of USDA-ARS evaluated the accuracy of USDA-ARS developed agricultural system model (RZWQM2) in developing irrigation water management information from easily collected soil-weather-crop data by farmers. The model was found to be suitable for developing crop water requirement information from these data. The crop coefficients developed with this model can be combined with the ‘maximum possible crop water demand’ at any location, calculated from commonly measured weather data for developing irrigation scheduling information for farmers.
Technical Abstract: Accurate quantification of crop evapotranspiration (ET) is critical in optimizing irrigation water productivity, especially, in the semiarid regions of the world where limited rainfall is supplemented by irrigation for profitable crop production. In this context, cropping system models are potential tools for predicting ET or crop water requirements in agriculture across soils and climates and assist in developing a decision support tools for irrigation. The objective of this study was to evaluate the accuracy of RZWQM2 simulated ET for irrigated silage and grain corn against measured crop water use and soil evaporation with large weighing lysimeters in the Texas High Plains in 1990, 2006 and 2007. An extended approach, based on the Shuttleworth and Wallace method (extended S-W), was used to estimate potential crop ET (E and T separately) demand in RZWQM2. The Nimah and Hanks approach was used for crop water uptake and Richard’s Equation for soil water redistribution modeling. Simulations of biomass, leaf area index, soil water storage, and ET were found to be reasonably accurate for irrigation water management applications. Relative Root Mean Square Deviations (RRMSD) of simulations of corn biomass were between 11 and 21%, LAI between 8 and 18%, water in the soil profile between 4 and 6%, and actual crop ET between 22 and 28% across the three years of measured data. Fallow soil evaporations before and after corn planting also were simulated with a high level of accuracy verifying the robustness of the model in simulations across cropping seasons. The crop coefficients (Kc) calculated with the short and tall crop reference ET methods varied from year to year. The extended S-W approach showed the capability to estimate potential crop ET comparable to the lysimetric measurements, i.e., without the use of Kc. Further work on the parameterization of the dynamic canopy surface resistances is expected to improve further the results.